Community-centred approach for assessing social sustainability in mining regions : a case study of Chingola district, Zambia

DATA AVAILABILITY STATEMENT : The datasets generated for this study are available on request to the corresponding author.Social sustainability has received the least attention among the three-pillars of sustainable development in the mining regions of Africa. However, with the rapid population growth globally, social sustainability assessment using clearly defined indicators is becoming essential to ensuring urban sustainability, specifically in mining regions. This study assessed the contribution of the mining industry to the social sustainability of the Chingola district in terms of cumulative impacts and the extent to which CSR initiatives have contributed to a social sustainability profile. Mixed methods including in-depth key informant interviews guide and observation were used to collect data on social sustainability. The data were collected from 10 out of the 28 wards of Chingola, selected using a systematic random sampling. A total of 500 households of which 49 households (with a sampling unit of 10) and 10 key informants' stakeholders were purposively selected. Aggregation and normalization techniques were used to construct the composite indicators depicting the strength of each indicator. The social sustainability of the Chingola district based on the calculated composite indicators varies from weak-to-moderate sustainability. The proposed indicators could serve local government and mining companies, redirect development schemes, re-strategize the stakeholders' involvement, and support corporate governance.http://wileyonlinelibrary.com/journal/sdhj2023Plant Production and Soil Scienc

Land use/cover spatiotemporal dynamics, and implications on environmental and bioclimatic factors in Chingola district, Zambia

This study uses remote sensing and GIS techniques to examine the intensity and dynamics of land use/cover change and environmental indices across a four-decade period in the Chingola district of Zambia, from 1972 to 2020 using five classification stages (1972, 1992, 2001, 2013, and 2020). A total of 10 key climate change detection monitoring indices were generated using RClimDex to examine the implications of land degradation on the bioclimatic factors from 1983 to 2020. The findings revealed a significant expansion in Built-ups (7.3%/year), farmlands (3.18%/year), and mining areas (0.82%/year) at the expense of natural resources. The highest human pressure was exerted on Savannah woodlands (−0.78), through agriculture (0.76) and infrastructure development (0.44) between 1992 and 2001.The analysis of the bioclimatic indices revealed a significant decline in rainfall quantity and intensity, and a rising in temperature (warmer days and nights). The Annual rainfall has decreased by −3.25%, while the potential evapotranspiration has increased by 0.04% from 1983 to 2020, resulting in an Aridity Index of 0.60 and a moisture deficit index of −0.42. To offset agriculture’s propensity to spatially expand and further encroach into savannah woodlands and forests, urban containment policies and programs that stimulate agricultural intensification are needed to reduce urban sprawl and protect the city’s remaining forestlands.The World Bank financially supported this research through the African Centre of Excellence on Sustainable Mining (ACESM) Scholarship program of Copperbelt University.https://www.tandfonline.com/loi/tgnh20hj2023Plant Production and Soil Scienc

Application of Conservation and Veterinary Tools in the Management of Stray Wildlife in Zambia

In recent years, Zambia has seen an increase in the incidences of conflicts involving stray wild animals with humans. Notable among these animals include the African elephants, buffalo and lion. Consequently, this triggers a response from law enforcement units of both government and the department of national parks to control the animals. Regrettably, most of their responses have involved the shooting down of these animals, rather than taking advantage of conservation and veterinary tools to relocate the animals back into protected areas, and this raises concerns about the welfare of these animals. Therefore, this chapter will bring to light some of the locally available tools that could be used to control stray wildlife in order to contribute towards both conservation and reducing human-wildlife conflicts in Zambia

Future trade-offs to 2050: land cover change, biodiversity and agriculture in Zambia

In Zambia, demand for cereal to feed a growing population is predicted to double by 2050. As pressure on agricultural land increases, there is an urgent need to develop tools to minimise conflicts between competing land uses

Biomass modelling of selected drought tolerant Eucalypt species in South Africa

Thesis (MScFor)--Stellenbosch University, 2013.ENGLISH ABSTRACT: The study aims at developing models for predicting aboveground biomass for selected drought tolerant Eucalyptus (E) species (E. cladocalyx, E. gomphocephala and E. grandis x camaldulensis) from the dry west coast. Biomass models were fit for each of the species and a cross-species model was parameterised based on pooled data for all the three species. Data was based on destructive sampling of 28 eucalypt trees which were 20 years of age and additional five five-year old E. gomphocephala trees. Preliminary measurements on diameter at breast height (dbh), height (h) and crown height were recorded in the field. The sampled trees were then felled and samples of discs, branches and foliage were collected. Density of the wood discs and the bark was determined by a water displacement method and computer tomography scanning (CT-scanner). Stem biomass was reconstructed using Smalian’s formula for volume determination and the calculated densities. Upscaling of the crown was carried out by regression equations formulated by employing the sampled branches. Further assessment was carried out on a sub-sample by subjecting the samples to different drying temperatures in a series between 60 and 105ºC. Linear models were parameterised by a simultaneous regression approach based on Seemingly Unrelated Regression (SUR) using the “Systemfit” R statistical package. The predictor variables employed in the study were dbh, d2h and h in which the coefficient of determination (R2), Mean Standard Error (MSE) and Root Mean Standard Error (RMSE) were used to determine the goodness of fit for the models. Akaike Information Criteria (AIC) was also used in the selection of the best fitting model. A system of equations consisting of five models was formulated for each Eucalyptus species. The biomass prediction models had degrees of determination (R2) ranging from 0.65 to 0.98 in which dbh and d2h were the main predictor variable while h improved the model fit. The total biomass models were the best fitting models in most cases while foliage biomass had the least good fit when compared to other models. When the samples were subjected to different drying temperatures, stem wood had the largest percentage change of 6% when drying from 60ºC to 105ºC while foliage had the lowest percentage change of less than 2%.AFRIKAANSE OPSOMMING: Die doel met hierdie studie is om modelle vir die voorspelling van die bogrondse biomassa van drie droogte-bestande Eucalyptus (E) spesies (E. cladocalyx, E. gomphocephala en E. grandis x camaldulensis), gekweek op die droë kusvlakte in Wes-Kaapland, te ontwikkel. Biomassa modelle vir elk van die spesies is gepas en ’n model gegrond op die gekombineerde data van al drie die spesies, is geparameteriseer. Verder is die biomassa variasie onder verskeie droogingstemperature vasgestel. Die data versameling is uitgevoer gegrond op die destruktiewe mostering van 28 Eucalyptus bome wat 20 jaar oud was en ’n bykomende vyf vyfjarige E. gomphocephala bome. Die aanvanklike mates, naamlik deursnee op borshoogte (dbh), boomhoogte (h) en kroonhoogte is in die veld opgemeet. Die gemonsterde bome is afgesaag en monsters van stamhout skywe, takke en die bas is versamel. Die digtheid van die skywe en die bas is deur die waterverplasing metode, en Rekenaar Tomografie skandering (“CT-scanning”) vasgestel. Stam biomassa is rekonstrukteer deur gebruik te maak van Smalian se formule vir die vasstelling van volume en berekende digtheid. Die opskaal van die kroon biomassa is gedoen met behulp van regressie vergelykings van gekose takmonsters. Submonsters is onderwerp aan ’n reeks van verskillende drogingstemperature tussen 60 en 105ºC. Lineêre modelle is deur ’n gelyktydige regressie benadering gegrond op die Seemingly Unrelated Regression (SUR) wat ’n“Systemfit” R statistiese pakket gebruik, parameteriseer. Die voorspeller veranderlikes wat in hierdie studie gebruik is, is dbh, d2h en h waarin die koëffisient van bepaling (R2), gemiddelde standaardfout (MSE) en vierkantswortel van die gemiddelde standaardfout (RMSE) gebruik is om vas te stel hoe goed die model pas. Akaike Inligting Kriteria is gebruik vir die seleksie van die gepaste model. ’n Reeks vergelykings wat bestaan uit vyf modelle is vir elke Eucalyptus spesie geformuleer. Die biomassa voorspelling model het waardes vir die koëffisiente van bepaling (R2) opgelewer wat strek van 0.65 to 0.98% en waarin dbh en d2h die hoof voorspelling veranderlikes is, terwyl h die pas van die model verbeter. Die totale biomassa model het in die meeste gevalle die beste gepas en die blaarbiomassa die swakste as dit met die ander modelle vergelyk word. Tydens droging vind die grootste persentasie verandering van 6% by stamhout plaas tussen temperature van 60ºC tot 105ºC, en die kleinste persentasie verandering van minder as 2% by blare

Sentinel-2 Data for Land Cover/Use Mapping: A Review

The advancement in satellite remote sensing technology has revolutionised the approaches to monitoring the Earth’s surface. The development of the Copernicus Programme by the European Space Agency (ESA) and the European Union (EU) has contributed to the effective monitoring of the Earth’s surface by producing the Sentinel-2 multispectral products. Sentinel-2 satellites are the second constellation of the ESA Sentinel missions and carry onboard multispectral scanners. The primary objective of the Sentinel-2 mission is to provide high resolution satellite data for land cover/use monitoring, climate change and disaster monitoring, as well as complementing the other satellite missions such as Landsat. Since the launch of Sentinel-2 multispectral instruments in 2015, there have been many studies on land cover/use classification which use Sentinel-2 images. However, no review studies have been dedicated to the application of ESA Sentinel-2 land cover/use monitoring. Therefore, this review focuses on two aspects: (1) assessing the contribution of ESA Sentinel-2 to land cover/use classification, and (2) exploring the performance of Sentinel-2 data in different applications (e.g., forest, urban area and natural hazard monitoring). The present review shows that Sentinel-2 has a positive impact on land cover/use monitoring, specifically in monitoring of crop, forests, urban areas, and water resources. The contemporary high adoption and application of Sentinel-2 can be attributed to the higher spatial resolution (10 m) than other medium spatial resolution images, the high temporal resolution of 5 days and the availability of the red-edge bands with multiple applications. The ability to integrate Sentinel-2 data with other remotely sensed data, as part of data analysis, improves the overall accuracy (OA) when working with Sentinel-2 images. The free access policy drives the increasing use of Sentinel-2 data, especially in developing countries where financial resources for the acquisition of remotely sensed data are limited. The literature also shows that the use of Sentinel-2 data produces high accuracies (>80%) with machine-learning classifiers such as support vector machine (SVM) and Random forest (RF). However, other classifiers such as maximum likelihood analysis are also common. Although Sentinel-2 offers many opportunities for land cover/use classification, there are challenges which include mismatching with Landsat OLI-8 data, a lack of thermal bands, and the differences in spatial resolution among the bands of Sentinel-2. Sentinel-2 data show promise and have the potential to contribute significantly towards land cover/use monitoring

Monitoring land cover dynamics for Zambia using remote sensing: 1972–2016.

Global land cover change is characterised by the expansion of agricultural and urban areas, which results in forest loss, especially in sub-Saharan African countries such as Zambia. This topic has received increasing research attention due to the close relationship between land cover and land use, food security and climate change. The Zambian landscape has high rates of land cover change associated with deforestation, forest degradation and urbanisation. With these changes, managing natural resources in Zambia requires reliable information with which to make informed decisions during land use planning. However, existing land cover information for Zambia is limited in its spatial and temporal scales. The availability of remotely sensed data with an open access policy and a long historical record, such as Landsat satellite imagery, offers opportunities for monitoring long-term land cover change over large areas. This thesis aims to provide an understanding of the different aspects of remotely sensed land cover monitoring, including image pre-processing, land cover classification, land cover change and factors associated with land cover change in Zambia. This research was conducted at a national scale over a period of four decades (1972–2016). The current study started with a detailed literature review on the development of the methods of Landsat land cover classification, which was followed by testing machine-learning classifiers and pre-processing methods on pansharpened and non-pansharpened Landsat Operational Land Imager (OLI-8) images. Classification of nine land cover types (primary forest, secondary forest, plantation forest, wetlands, cropland, irrigated crops, grassland, waterbodies and settlements) was conducted for six time steps (1972, 1984, 1990, 2000, 2008, and 2016), which were chosen by considering past economic and political events in Zambia. Post-classification analysis was then applied in order to understand changes in land cover. Finally, the factors contributing to land cover change were assessed using a classification tree (CT) approach. The literature review (Chapter 2) showed that Landsat land cover classification methods have developed from manual delineation to advanced computer-based classification methods. These developments have occurred due to the advancements in computer science (e.g. machine- learning and artificial intelligence) and improvements in remotely sensed data acquisition. To attain high land cover classification accuracies, Landsat images require the selection of an effective classification method and the application of pre-processing methods. The combination of object-based image analysis (OBIA) and machine-learning classifiers, such as random forests (RF), has become more common than the pixel-based approach. The assessment of pre-processing methods (Chapter 3) on two provinces of Zambia (Copperbelt and Central), which were covered by four Landsat OLI-8 images, indicated that applying both atmospheric and topographic correction improved classification accuracy. The results showed that non-pre-processed images reached a classification accuracy of 68% for pansharpened and 66% for standard Landsat OLI-8 images. Classification accuracy improved to 93% (pansharpened) and 86% (standard) when combined moderate-resolution atmospheric transmission (MODTRAN) and cosine topographic correction pre-processing were applied. The results showed that image corrections are more important when applied on multiple scenes, especially for time series studies. The results also identified that the RF classifier outperformed the other classifiers by attaining an overall accuracy of 96%. These results informed the choice of pre-processing and classification analyses to use for the subsequent land cover analysis. A nationwide land cover classification analysis was then undertaken for each of the six time steps (Chapter 4). Overall accuracies ranging from 79% to 86% were attained, with more recent time steps, captured by Landsat OLI-8 imagery, having the highest accuracy. The variation in classification accuracies was mainly attributed to the differences in spatial, spectral and radiometric resolutions of the satellite images available for each time step. This chapter also showed that 62.74% of the Zambian landscape experienced change. Primary forest declined from 48% to 16% between 1972 and 2016, while secondary forest increased from 16% to 39% during the same period. The results also showed that forests have been recovering by 0.03% to 1.3% yr⁻¹ (53,000–242, 000 ha yr⁻¹); however, these rates are lower than deforestation rates (−0.54% to −3.05% yr⁻¹: 83,000–453,000 ha yr⁻¹). Annual rates of change varied by land cover, with irrigated crops having the largest increase (+3.19% yr⁻¹) and primary forest having the greatest decrease (−2.48% yr⁻¹). Area of settlements, cropland and grasslands increased, while wetlands declined. Due to increased forest fragmentation, forest connectivity declined by 22%. The CT models for analysing the factors contributing to land cover change (Chapter 5) were produced with overall accuracies ranging from 70% to 86%. CTs are statistical approaches used to partition categorical data (response variables) into mutually exclusive subgroups using a set of explanatory variables. Here, the response variables included a binary scenario (change/no change) and changes from individual land covers. The explanatory variables were the different factors considered to be associated with the land cover changes. The major factors associated with the binary scenario (change or no change to 1972 land cover) were percentage of cultivated area, crop yield, and distance to waterbodies. Forest losses were mainly associated with crop yield, area under cultivation, population density and distance to roads and railways. An important insight from this chapter was the influence of protected areas (e.g. national forests) on forest reversion and recovery. Due to the national extent and long temporal record of land cover change, the findings from this thesis are important for land use planning in Zambia. This research not only documents deforestation and forest degradation occurring throughout Zambia over the past four decades, but also highlights the importance of increasing the extent of protected areas in order to support forest reversion and recovery. Since forests are an important component of climate change mitigation initiatives, these results will provide baseline information for international climate change mitigation initiatives such as reducing emissions from deforestation and forest degradation (REDD+)

Developments in Landsat Land Cover Classification Methods: A Review

Land cover classification of Landsat images is one of the most important applications developed from Earth observation satellites. The last four decades were marked by different developments in land cover classification methods of Landsat images. This paper reviews the developments in land cover classification methods for Landsat images from the 1970s to date and highlights key ways to optimize analysis of Landsat images in order to attain the desired results. This review suggests that the development of land cover classification methods grew alongside the launches of a new series of Landsat sensors and advancements in computer science. Most classification methods were initially developed in the 1970s and 1980s; however, many advancements in specific classifiers and algorithms have occurred in the last decade. The first methods of land cover classification to be applied to Landsat images were visual analyses in the early 1970s, followed by unsupervised and supervised pixel-based classification methods using maximum likelihood, K-means and Iterative Self-Organizing Data Analysis Technique (ISODAT) classifiers. After 1980, other methods such as sub-pixel, knowledge-based, contextual-based, object-based image analysis (OBIA) and hybrid approaches became common in land cover classification. Attaining the best classification results with Landsat images demands particular attention to the specifications of each classification method such as selecting the right training samples, choosing the appropriate segmentation scale for OBIA, pre-processing calibration, choosing the right classifier and using suitable Landsat images. All these classification methods applied on Landsat images have strengths and limitations. Most studies have reported the superior performance of OBIA on different landscapes such as agricultural areas, forests, urban settlements and wetlands; however, OBIA has challenges such as selecting the optimal segmentation scale, which can result in over or under segmentation, and the low spatial resolution of Landsat images. Other classification methods have the potential to produce accurate classification results when appropriate procedures are followed. More research is needed on the application of hybrid classifiers as they are considered more complex methods for land cover classification

Decision Tree Algorithms for Developing Rulesets for Object-Based Land Cover Classification

Decision tree (DT) algorithms are important non-parametric tools used for land cover classification. While different DTs have been applied to Landsat land cover classification, their individual classification accuracies and performance have not been compared, especially on their effectiveness to produce accurate thresholds for developing rulesets for object-based land cover classification. Here, the focus was on comparing the performance of five DT algorithms: Tree, C5.0, Rpart, Ipred, and Party. These DT algorithms were used to classify ten land cover classes using Landsat 8 images on the Copperbelt Province of Zambia. Classification was done using object-based image analysis (OBIA) through the development of rulesets with thresholds defined by the DTs. The performance of the DT algorithms was assessed based on: (1) DT accuracy through cross-validation; (2) land cover classification accuracy of thematic maps; and (3) other structure properties such as the sizes of the tree diagrams and variable selection abilities. The results indicate that only the rulesets developed from DT algorithms with simple structures and a minimum number of variables produced high land cover classification accuracies (overall accuracy > 88%). Thus, algorithms such as Tree and Rpart produced higher classification results as compared to C5.0 and Party DT algorithms, which involve many variables in classification. This high accuracy has been attributed to the ability to minimize overfitting and the capacity to handle noise in the data during training by the Tree and Rpart DTs. The study produced new insights on the formal selection of DT algorithms for OBIA ruleset development. Therefore, the Tree and Rpart algorithms could be used for developing rulesets because they produce high land cover classification accuracies and have simple structures. As an avenue of future studies, the performance of DT algorithms can be compared with contemporary machine-learning classifiers (e.g., Random Forest and Support Vector Machine)

Simulating Scenarios of Future Intra-Urban Land-Use Expansion Based on the Neural Network–Markov Model: A Case Study of Lusaka, Zambia

Forecasting scenarios of future intra-urban land-use (intra-urban-LU) expansion can help to curb the historically unplanned urbanization in cities in sub-Saharan Africa (SSA) and promote urban sustainability. In this study, we applied the neural network–Markov model to simulate scenarios of future intra-urban-LU expansion in Lusaka city, Zambia. Data derived from remote sensing (RS) and geographic information system (GIS) techniques including urban-LU maps (from 2000, 2005, 2010, and 2015) and selected driver variables, were used to calibrate and validate the model. We then simulated urban-LU expansion for three scenarios (business as usual/status quo, environmental conservation and protection, and strategic urban planning) to explore alternatives for attaining urban sustainability by 2030. The results revealed that Lusaka had experienced rapid urban expansion dominated by informal settlements. Scenario analysis results suggest that a business-as-usual setup is perilous, as it signals an escalating problem of unplanned settlements. The environmental conservation and protection scenario is insufficient, as most of the green spaces and forests have been depleted. The strategic urban planning scenario has the potential for attaining urban sustainability, as it predicts sufficient control of unplanned settlement expansion and protection of green spaces and forests. The study proffers guidance for strategic policy directions and creating a planning vision